arXiv:2606.16729v1 Announce Type: new Abstract: While there is an extensive body of work characterizing the sample complexity of discounted cumulative-reward MDPs, finite sample analyses for average-reward MDPs have been limited, and most existing works rely on restrictive assumptions such as ergodicity or access to a generative model. In this work, we establish the first finite sample complexity guarantees from a single trajectory for weakly communicating average-reward MDPs. To this end, we study the dynamics of a single trajectory in weakly communicating MDPs and based on this analysis, we
The continuous push for more efficient and robust reinforcement learning algorithms, especially with less data, drives research in this area.
Improved data efficiency in reinforcement learning, particularly for average-reward MDPs, can accelerate the development and deployment of autonomous systems in real-world applications.
The ability to learn effective policies from a single trajectory significantly reduces the data requirements for training complex AI agents.
- · AI developers
- · Robotics
- · Reinforcement learning researchers
- · Logistics and automation
More sample-efficient reinforcement learning algorithms will become available for practical applications.
This could lead to faster iteration and deployment of AI agents in environments where data collection is expensive or time-consuming.
Reduced data needs might democratize advanced AI development by lowering computational and data gathering barriers.
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